Geology Reports⌕ Search

Geology topics

Research about Everglades

Source-linked reports with geographic coverage including Everglades.

At least 19 recordsLinked to original sources
USGS2026

Predator hunting modes induce varied selection pressures on prey personality

Consistent individual differences in animal behavior—ie, personalities—have been documented in many taxa to have important fitness consequences in the context of predator-prey interactions. Multiple mechanisms have been suggested to maintain variation in prey personality traits, including life-history tradeoffs and divergent selection pressures from predators with different hunting strategies. However, empirical evidence for these hypotheses has been limited by a lack of data originating from complex multipredator systems in situ. To address this gap and evaluate these 2 mechanisms, we collected hispid cotton rats (Sigmodon hispidus) in the Florida Everglades and quantified 3 behavioral traits in the field. After releasing rats at their point of capture, we monitored their survival in their natural habitat where they faced threats from a diversity of ambush (snakes) and active predators (mammals; birds). Using Cox proportional hazards regression models and competing risks models, we assessed the effect of boldness, activity, and docility on rodent predation risk. Contrary to conventional predictions that bolder and more active individuals would face higher predation risk due to life-history tradeoffs, we found that the impact of these traits on prey survival varied with the predators' hunting modes: shyer, more active individuals were more likely to be killed by active predators, whereas less active rats appeared more likely to be killed by ambush predators. Our study supports the hypothesis that a diversity of predator hunting strategies may help maintain trait variation in prey populations.

Florida↗
USGS2026

A tool to monitor hydrologic conditions on tree islands in the Everglades

Tree islands are patchy upland forested habitats in Florida's Everglades that face degradation and disappearance due to altered hydrologic patterns. The U.S. Geological Survey coordinated with the Miccosukee Tribe of Indians of Florida and the Seminole Tribe of Florida to co-develop a decision-support tool based on tree-island hydrologic conditions. Everglades managers can use this tool to help with restoration planning and water operations decisions that affect tree-island conditions. After a series of organized workshops and meetings, a list of hydrologic metrics was selected as indicators of tree-island health, including hydroperiod, number of days since last dry, and maximum water depth at the head of the island. As a result, a web application tool, called ETree, has been developed and is publicly available online. This web application provides data on daily metrics for the current Everglades water year and annual summaries for past years, beginning in 2000.

Florida↗
USGS2025

Restoring the Florida Everglades: Insights on integrating sea level rise into decision-support tools

Although coastal ecosystems are impacted by climate change and sea-level rise, many ecological and hydrological models do not yet incorporate sea-level rise projections in their modeling outputs. Therefore, this research examined the various challenges that may prevent sea-level rise from being effectively incorporated in modeling and decision-support tools. We conducted semi-structured interviews with twenty-six professionals involved in Florida’s Everglades restoration. We applied the Diffusions of Innovations Theory to better understand factors that can impact practitioners’ adoption of newly designed decision-support tools that examine sea-level rise in the freshwater Everglades. The Diffusions of Innovations Theory provided insights into practitioners’ perceptions of these tools. We found that these practitioners have a strong interest in using dynamic decision-support tools to plan for sea-level rise impacts on Everglades restoration, particularly when they receive information at appropriate geographic and temporal scales and are given hands-on tools and training. However, challenges that prevent developing these tools include outdated data, limited organizational capacity and funding, limited use of long-term indicators, uncertainty about climate change impacts on local ecosystems, and lack of integration between hydrological and ecological models. Our research also highlights that greater availability of different types of tools can help to meet the needs of the scientific and non-scientific audiences involved in Everglades restoration.

Florida↗
USGS2025

Biophysical simulation of wetland surface water flow to predict changing water availability in the Everglades

A central challenge for water managers is to adaptively manage water availability to meet societal needs while simultaneously protecting ecosystems. Progress restoring the Everglades requires predictions of how overland flow of surface water can be increased to rehydrate and revive downstream areas without causing unintended harms. We developed a biophysical flow rate expression (BioFRE) that relates shallow overland flow to roughness dominated by spatially variable vegetation and microtopography. Hydraulic theory was combined with vegetation and topographic field data to quantify hydraulic roughness without calibrating the expression to fit hydrologic data. To assess changes in overland flow capacity, we benchmarked BioFRE against best available simulations of the historic Everglades and against present-day hydrologic data representing various levels of degradation. The simulations revealed that overland flow capacity of the Everglades in now half of what it was historically in the Everglades primarily because of the loss of deepwater sloughs. The relative sensitivity of simulated flows to the individual biophysical factors was quantified and related to habitat value and drought and flood resilience. Our approach can potentially be used in other flowing wetland and floodplain systems to understand and adaptively manage water and ecological resources.

Florida↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗
USGS2024

High-Flow Experimental Outcomes to Inform Everglades Restoration, 2010–22

The Decompartmentalization Physical Model (DPM) was an experimental facility in the central Everglades operated between 2010 and 2022 to release high flows through a levee-enclosed area of degraded ridge and slough wetland that had been isolated from flow for sixty years. The purpose of DPM experimental program was to make measurements before, during, and after seasonal high-flow releases that could help guide the Congressionally authorized Everglades restoration project known as the Decompartmentalization and Sheet Flow Enhancement Project. The DPM facility was operated by the South Florida Water Management District, with the U.S. Geological Survey (USGS) and several universities participating in experimental design and leading aspects of the research. The USGS research at DPM focused on measuring high-flow hydraulics and its sedimentary and ecological responses in downstream wetlands. USGS investigated interactions between flow and vegetation and microtopography that influenced flow velocity and water depth, bed shear stress, sediment entrainment, and the resulting downstream transport of suspended sediment and fate of particle-associated phosphorus. USGS also investigated high-flow changes in water-column mixing and gas exchange and resulting effects on metabolism of the aquatic ecosystem (primary productivity and respiration). USGS also investigated effects of built structures such as levee gaps that were constructed to reconnect levee-enclosed basins. This report describes the methods and results of the USGS-led data collection at DPM. The USGS studies at DPM have identified factors that influence effectiveness of restoration, specifically how high-flow releases maximize sheet flow and affect sediment and nutrient dynamics while minimizing undesirable outcomes caused by past management that bypassed wetlands by conveying polluted water through canals to ecologically sensitive downstream areas. The DPM high-flow experiments reconnected the Water Conservation Area 3A and Water Conservation Area 3B basins, and it therefore has become a central feature of the restoration’s Decompartmentalization and Sheet Flow Enhancement Project. DPM’s scientific findings have already influenced the adaptive management of Everglades restoration in guiding elements of the final design and implementation of the Central Everglades Planning Project-South. In addition to serving Everglades restoration, the DPM has the potential to influence similar adaptive management programs throughout the nation’s network of federal and state-managed river corridors, floodplains, and riparian ecosystems.

Florida↗
USGS2024

Simulated sea level rise in coastal peat oils stimulates mercury methylation

Coastal wetlands are vulnerable to sea level rise with unknown consequences for mercury (Hg) cycling, particularly the potential for exacerbating neurotoxic methylmercury (MeHg) production and bioaccumulation in food webs. Here, the effect of sea level rise on MeHg formation in the Florida Everglades was evaluated by incubating peat cores from a freshwater wetland for 0–20 days in the laboratory at five salinity conditions (0.16–6.0 parts-per-thousand; 0.20–454 mg L –1 sulfate (SO 4 2– )) to simulate the onset of sea level rise within coastal margins. Isotopically enriched inorganic mercury ( 201 Hg(II)) was used to track MeHg formation and peat-porewater partitioning. In all five salinity treatments, porewaters became anoxic within 1 day and became progressively enriched in dissolved organic matter (DOM) of greater aromatic composition over the 20 days compared to ambient conditions. In the four highest salinity treatments, SO 4 2– concentrations decreased and sulfide concentrations increased over time due to microbial dissimilatory SO 4 2– reduction that was concurrent with 201 Hg(II) methylation. Importantly, elevated salinity resulted in a greater proportion of produced Me 201 Hg observed in porewaters as opposed to bound to peat, interpreted to be due to the complexation of MeHg with aromatic DOM released from peat. The findings highlight the potential for enhanced production and mobilization of MeHg in coastal wetlands of the Florida Everglades due to the onset of saltwater intrusion.

Florida↗
USGS2024

Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network

The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero. The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period. Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.

Florida↗
USGS2024

Flooding-induced failure of an invasive Burmese Python nest in southern Florida

It is important to understand the factors affecting the reproductive success of an invasive species to estimate population size and develop management plans. There remains much we do not understand about the reproductive biology of invasive Burmese Pythons in both their native and invasive range. Oviposition site selection is an important factor in determining reproductive success as nesting is a vulnerable period in the life of reptiles. Flooding can be particularly influential for nesting outcomes and success of developing embryos in habitats that experience periods of heavy rainfall or seasonal flooding. It is not clear how seasonal flooding may impact oviposition site selection by females or hatchling survival of Burmese Pythons in Florida. On 10 May 2023, a radio-transmitted female python oviposited in a hollow log and during incubation all eggs were completely submerged for variable amounts of time. All eggs were collected (n = 78) and dissected to confirm fertilization and assess the stage of embryo development. Two eggs were found to be infertile, while the remaining fertile eggs contained embryos that died at various stages of development. The observed failure of this python nest was a direct result of oviposition site selection by the female. The frequency at which females lay eggs in suboptimal locations in southern Florida is currently unknown. This maladaptive behavior would suggest Burmese Pythons have fitness levels below theoretical optima.

Florida↗
USGS2023

Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA

Water level is an important guide for water resource management and wetland ecosystems, defining one of the most basic processes in hydrology. This research seeks to investigate the possibility of complementing numerical modeling with a Machine Learning (ML) model to forecast daily water levels in the southern Everglades in Florida, USA. An exact analytical solution to water level may not be possible, but using the computational methods afforded by ML, the traditional numerical techniques may be enhanced to generate more robust, scalable predictions. Five locations were chosen for application of the Time-Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) ML models, which were built to estimate water level with 1, 2, 3, 7 and 10 day forecasts using a simulation step of 1 day. The results showed that rainfall forecasts from weather models could improve water-level forecasts if the accuracy and performance of the weather models can be improved. The ML models presented here improve water-level predictions from a historical hydrologic model for a 24 hour forecast horizon.

Florida↗
USGS2023

Bobcat occupancy, tree islands, and invasive Burmese pythons in an Everglades conservation area

Bobcats ( Lynx rufus ) are terrestrial mammals that also inhabit tree islands (i.e., topographically elevated patches of forested land) embedded in the subtropical Everglades wetlands, which serve as a dry refuge habitat during the wet season in this region of Florida, USA. The Comprehensive Everglades Restoration Plan seeks to restore Everglades water flow to pre-drainage conditions, but little is known about how water levels or other landscape-level factors may influence mammalian occurrence, such as bobcats, on the tree islands in this ecosystem. We used game camera records and occupancy modeling to test for effects of static habitat variables and dynamic hydrologic variables. We hypothesized that deep water levels would limit the accessibility of tree islands to bobcats; therefore, we predicted that bobcat occupancy would decline with higher water levels. We also tested for the effect of an expanding invasive snake (i.e., Burmese python [ Python molarus bivittatus ]) using output from a model constructed to predict density and spread of Burmese pythons across southern Florida. We hypothesized that increases in Burmese pythons on the landscape would influence the food resources of bobcats, resulting in reduced bobcat occupancy at higher predicted densities of pythons. We built detection histories using 1,855 bobcat images from game cameras set on 87 tree islands in an Everglades conservation area from 2005–2019. Bobcat occupancy was significantly diminished when predicted Burmese python densities exceeded approximately 3 Burmese pythons/km 2 . Bobcat occupancy probability also increased with tree-island density around the focal tree island. Although water depth and hydroperiod surrounding tree islands appeared in our top 3 candidate models, the hydrologic variables had weak effects on bobcat occupancy. Our results suggest that while hydrologic dynamics may play a role, the invasive Burmese python has stronger influences on bobcat occupancy of tree islands in this Everglades conservation area.

Florida↗
USGS2023

Genetic analysis of federally endangered Cape Sable seaside sparrow subpopulations in the Greater Everglades, USA

The federally endangered Cape Sable seaside sparrow ( Ammospiza maritima mirabilis ) is endemic to the Greater Everglades ecosystem in southern Florida, inhabiting fragmented marl prairies in six individual subpopulations. The subspecies is threatened by loss of breeding habitat from fire and water management. Genetic information is severely limited for the subspecies but could help inform decisions regarding subpopulation protections and potential translocations for genetic rescue. To provide genetic data and inform management efforts, feather samples were collected across five subpopulations (designated A–E) and protocols were tested to optimize DNA extraction yields. We assessed four mitochondrial DNA markers (N = 36–69) and 12 nuclear microsatellite loci (N = 55) in 108 sparrows. Mitochondrial DNA sequences revealed low haplotype diversity, with NADH dehydrogenase-2 haplotypes matching to most other extant subspecies and to the Atlantic coast subspecies. Nuclear diversity was low compared to other subspecies, but similar across subpopulations. Samples grouped as one population when analyzed by Principal Component Analysis, Bayesian modelling and genetic distance metrics. Limited genetic emigration was detected from one putative migrant. Relatedness was significantly different for sparrows in the most geographically distant subpopulation (A), likely reflecting high self-recruitment and natal site fidelity ( P = 0.003). The low to moderate effective population size (N E = 202.4; N E :N C = 0.06) and generation time estimates indicated that unique genetic variation could be lost quickly during stochastic events. The sample sizes were limited, which reduced the power to comprehensively address recent population size reductions and any subsequent loss of genetic diversity.

Florida↗
USGS2023

The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration

Understanding of the Everglades’ ecological vulnerabilities and restoration needs has advanced over the past decade but has not been applied in an integrated manner. To address this need, we developed the Everglades Vulnerability Analysis (EVA), a decision support tool that uses modular Bayesian networks to predict the ecological outcomes of a subset of the ecosystem’s health indicators. This tool takes advantage of the extensive modeling work already done in the Everglades and synthesizes information across indicators of ecosystem health to forecast long-term, landscape-scale changes. In addition, the tool can predict indicator vulnerability through comparison to user-defined ideal system states that can vary in the level of certainty of outcomes. An integrated understanding of the Everglades system is essential for evaluation of trade-offs at local, regional, and system-wide scales. Through EVA, Everglades restoration decision makers can provide effective guidance during restoration planning and implementation processes to mitigate unintended consequences that could result in further damage to the Everglades system.

Florida↗
USGS2023

Environmental formation of methylmercury is controlled by synergy of inorganic mercury bioavailability and microbial mercury-methylation capacity

Methylmercury (MeHg) production is controlled by the bioavailability of inorganic divalent mercury (Hg(II) i ) and Hg-methylation capacity of the microbial community (conferred by the hgcAB gene cluster). However, the relative importance of these factors and their interaction in the environment remain poorly understood. Here, metagenomic sequencing and a full-factorial MeHg formation experiment were conducted across a wetland sulfate gradient with different microbial communities and pore water chemistries. From this experiment, the relative importance of each factor on MeHg formation was isolated. Hg(II) i bioavailability correlated with the dissolved organic matter composition, while the microbial Hg-methylation capacity correlated with the abundance of hgcA genes. MeHg formation responded synergistically to both factors. Notably, hgcA sequences were from diverse taxonomic groups, none of which contained genes for dissimilatory sulfate reduction. This work expands our understanding of the geochemical and microbial constraints on MeHg formation in situ and provides an experimental framework for further mechanistic studies.

Florida↗
USGS2023

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons ( Python molurus bivittatus ) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control.

Florida↗
USGS2023

Natives bite back: Depredation and mortality of invasive juvenile Burmese pythons (Python bivittatus) in the Greater Everglades Ecosystem

Burmese pythons (Python bivittatus Kuhl, 1820) are one of the world’s largest snake species, making them a highly successful and biologically damaging invasive predator in the Greater Everglades Ecosystem, Florida, USA. Though we have knowledge of python diet within this system, we understand very little of other interactions with native species. Effects native species have on invasive pythons, especially in the juvenile size class, are of particular interest as the prevalence of mortalities would inform potential population growth and trophic dynamics with native prey species. Native ophiophagous predators in Florida feed on smaller native snake species and it is unknown if they consistently recognize similarly sized juvenile invasive pythons as prey items. Using radiotelemetry, we found at least four native species within Big Cypress National Preserve that were implicated in juvenile python deaths, including three Florida cottonmouths (Agkistrodon conanti Gloyd, 1969), five American alligators (Alligator mississippiensis Daudin, 1802), one hispid cotton rat (Sigmodon hispidus Say and Ord, 1825), and three mesomammals. One mortality was the result of an attempt to subdue a prey item 106% the size of the python, constituting the largest predator:prey size ratio ever reported in this size class. This finding may indicate that phenotypic variation in individual juvenile pythons includes behavior that could be maladaptive within the novel Florida environment. Here we describe some of the first confirmed cases of non-anthropogenic mortality in juvenile Burmese pythons in Florida and present evidence that invasive pythons in this size class are now being incorporated into the diets of native species in its invasive range Burmese pythons (Python bivittatus Kuhl, 1820) are one of the world’s largest snake species, making them a highly successful and biologically damaging invasive predator in the Greater Everglades Ecosystem, Florida, USA. Though we have knowledge of python diet within this system, we understand very little of other interactions with native species. Effects native species have on invasive pythons, especially in the juvenile size class, are of particular interest as the prevalence of mortalities would inform potential population growth and trophic dynamics with native prey species. Native ophiophagous predators in Florida feed on smaller native snake species and it is unknown if they consistently recognize similarly sized juvenile invasive pythons as prey items. Using radiotelemetry, we found at least four native species within Big Cypress National Preserve that were implicated in juvenile python deaths, including three Florida cottonmouths (Agkistrodon conanti Gloyd, 1969), five American alligators (Alligator mississippiensis Daudin, 1802), one hispid cotton rat (Sigmodon hispidus Say and Ord, 1825), and three mesomammals. One mortality was the result of an attempt to subdue a prey item 106% the size of the python, constituting the largest predator:prey size ratio ever reported in this size class. This finding may indicate that phenotypic variation in individual juvenile pythons includes behavior that could be maladaptive within the novel Florida environment. Here we describe some of the first confirmed cases of non-anthropogenic mortality in juvenile Burmese pythons in Florida and present evidence that invasive pythons in this size class are now being incorporated into the diets of native species in its invasive range.

Florida↗